rezakj/scSeqR: scSeqR; a toolkit to analyze single cell sequencing data types (i.e scRNA-seq) and large numeric matrix files.

scSeqR (Single Cell Sequencing R package) is an R package with 2D and 3D interactive visualizations to works with high-throughput single cell sequencing technologies (i.e scRNA-seq, VDJ-seq and CITE-seq). As some research studies require a more attuned forms of normalization or spike-in normalization in some cases, scSeqR allows the users to chose from multiple normalization methods and correcting for dropouts (nonzero events counted as zero). Because some of the cell types are more challenging to work with, scSeqR also allows the users to choose from different clustering algorithms (i.e. ward.D, kmeans, ward.D2, hierarchical, etc.) and indexing methods (i.e. silhouette, ccc, kl, gap-stats, etc.) to adjust for sensitivity and stringency in order to find less or more subpopulations of cell types to design both unsupervised and supervised models to best suit your research. scSeqR provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells and genes, cell helth and cell cycle, merging, normalizing for dropouts and batch differences, pathway analysis, cell type prediction and tools to find marker genes for clusters and conditions. scSeqR inputs single cell data in 10X format, large numeric matrix files or standard data frames.

install.packages("remotes")
remotes::install_github("rezakj/scSeqR")